Member communication reply score calculation

ABSTRACT

In an example embodiment, a supervised machine learning algorithm is used to train a communication reply score model based on an extracted first set of features and second set of features from social networking service member profiles and activity and usage information. When a plurality of member search results is to be displayed, for the member identified in each of the plurality of member search results, the member profile corresponding to the member is parsed to extract a third set of one or more features from the member profile, activity and usage information pertaining to actions taken by the members on the social networking service is parsed to extract a fourth set of one or more features, and the extracted third set of features and fourth set of features is inputted into the communication reply score model to generate a communication reply score, which is displayed visually to a searcher.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior application Ser. No.14/975,756, filed on Dec. 19, 2015, which is incorporated by referenceherein in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to computer technology forsolving technical challenges in electronic communications. Morespecifically, the present disclosure relates to the calculation of acommunication reply score for members of an online network.

BACKGROUND

The rise of the Internet has given rise to two disparate phenomena: theincrease in the presence of social networks, with their correspondingmember profiles visible to large numbers of people, and the increase inthe desirability of reaching out to small groups of social networkmembers who meet strict criteria. This is especially pronounced in thefield of recruiting, where recruiters are typically attempting to findmembers with particular qualifications (e.g., education, experience,skills, etc.) and then generally the recruiters reach out to memberswith the particular qualifications to find out whether or not themembers may be willing to apply for the job openings the recruiter hasavailable.

Job solicitation communications, such as emails sent by recruiters tomembers who may be prospective job applicants, can take a lot of time onthe part of the recruiters, especially if done effectively. Effectivejob solicitation communications generally include personalizedinformation about the member and have the solicitation gearedspecifically towards that member, thus making it look less like a masscommunication sent to many potential applications and more like therecruiter has specifically targeted the member. Recruiters, however,have a limited amount of time to spend in creating such job solicitationcommunications, and thus would benefit greatly if presented withinsights as to how likely a particular member is to respond to such ajob solicitation communication. A technical problem arises, however, indetermining whether a particular member, gauged from informationavailable to a computer system, is likely to respond to a particularcommunication.

Another technical problem that arises is that, even if a recruiter werepresented with information about the chances that a particular memberwill reply to a job solicitation email, unless this information ispresented visually in an effective way via a user interface, theinformation may not be utilized correctly.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the technology are illustrated, by way of exampleand not limitation, in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating a client-server system, inaccordance with an example embodiment.

FIG. 2 is a block diagram showing the functional components of a socialnetworking service, including a data processing module referred toherein as a search engine, for use in generating and providing searchresults for a search query, consistent with some embodiments of thepresent disclosure.

FIG. 3 is a block diagram illustrating an application server module ofFIG. 2 in more detail.

FIG. 4 is a block diagram illustrating the communication reply scoregenerator of FIG. 3 in more detail, in accordance with an exampleembodiment.

FIG. 5 is a screen capture illustrating an example user interface, inaccordance with an example embodiment.

FIG. 6 is a screen capture illustrating a user interface, in accordancewith another example embodiment.

FIG. 7 is a screen capture illustrating a user interface, in accordancewith another example embodiment.

FIG. 8 is a flow diagram illustrating a method, in accordance with anexample embodiment, for providing an indication of a probability that amember of a social networking service will respond to an electroniccommunication sent via the social networking service.

FIG. 9 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 10 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION Overview

The present disclosure describes, among other things, methods, systems,and computer program products that individually provide functionalityfor speeding data access. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various aspects of different embodimentsof the present disclosure. It will be evident, however, to one skilledin the art, that the present disclosure may be practiced without all ofthe specific details.

In an example embodiment, a platform is provided that utilizesinformation available to a computer system to evaluate a likelihood thata particular social network member will respond to a particularcommunication. In another example embodiment, a specific user interfaceis provided to present this likelihood to a recruiter or otherinterested party in a manner that is effective in quickly conveying thelikelihood of the member and other members replying to a communication.

It should be noted that the term “social” as used throughout thisdocument should be interpreted broadly to cover any type of grouping ofonline members of a service in which communications can be sent throughthe service. This is in contrast to a grouping of online members ofservices where communications are only sent through external means(e.g., traditional email, phone call, etc.), and also in contrast togroupings of general Internet users.

FIG. 1 is a block diagram illustrating a client-server system 100, inaccordance with an example embodiment. A networked system 102 providesserver-side functionality via a network 104 (e.g., the Internet or awide area network (WAN)) to one or more clients. FIG. 1 illustrates, forexample, a web client 106 (e.g., a browser) and a programmatic client108 executing on respective client machines 110 and 112.

An application program interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application server(s) 118host one or more applications 120. The application server(s) 118 are, inturn, shown to be coupled to one or more database servers 124 thatfacilitate access to one or more databases 126. While the application(s)120 are shown in FIG. 1 to form part of the networked system 102, itwill be appreciated that, in alternative embodiments, the application(s)120 may form part of a service that is separate and distinct from thenetworked system 102.

Further, while the client-server system 100 shown in FIG. 1 employs aclient-server architecture, the present disclosure is, of course, notlimited to such an architecture, and could equally well find applicationin a distributed, or peer-to-peer, architecture system, for example. Thevarious applications 120 could also be implemented as standalonesoftware programs, which do not necessarily have networkingcapabilities.

The web client 106 accesses the various applications 120 via the webinterface supported by the web server 116. Similarly, the programmaticclient 108 accesses the various services and functions provided by theapplication(s) 120 via the programmatic interface provided by the APIserver 114.

FIG. 1 also illustrates a third party application 128, executing on athird party server 130, as having programmatic access to the networkedsystem 102 via the programmatic interface provided by the API server114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by a third party. The thirdparty website may, for example, provide one or more functions that aresupported by the relevant applications 120 of the networked system 102.

In some embodiments, any website referred to herein may comprise onlinecontent that may be rendered on a variety of devices including, but notlimited to, a desktop personal computer (PC), a laptop, and a mobiledevice (e.g., a tablet computer, smartphone, etc.). In this respect, anyof these devices may be employed by a user to use the features of thepresent disclosure. In some embodiments, a user can use a mobile app ona mobile device (any of the machines 110, 112 and the third party server130 may be a mobile device) to access and browse online content, such asany of the online content disclosed herein. A mobile server (e.g., APIserver 114) may communicate with the mobile app and the applicationserver(s) 118 in order to make the features of the present disclosureavailable on the mobile device.

In some embodiments, the networked system 102 may comprise functionalcomponents of a social networking service. FIG. 2 is a block diagramshowing the functional components of a social networking service,including a data processing module referred to herein as a search engine216, for use in generating and providing search results for a searchquery, consistent with some embodiments of the present disclosure. Insome embodiments, the search engine 216 may reside on the applicationserver(s) 118 in FIG. 1. However, it is contemplated that otherconfigurations are also within the scope of the present disclosure.

As shown in FIG. 2, a front end may comprise a user interface module(e.g., a web server 116) 212, which receives requests from variousclient computing devices, and communicates appropriate responses to therequesting client devices. For example, the user interface module(s) 212may receive requests in the form of Hypertext Transfer Protocol (HTTP)requests or other web-based API requests. In addition, a memberinteraction detection module 213 may be provided to detect variousinteractions that members have with different applications 120,services, and content presented. As shown in FIG. 2, upon detecting aparticular interaction, the member interaction detection module 213 logsthe interaction, including the type of interaction and any metadatarelating to the interaction, in a member activity and behavior database222.

An application logic layer may include one or more various applicationserver modules 214, which, in conjunction with the user interfacemodule(s) 212, generate various user interfaces (e.g., web pages) withdata retrieved from various data sources in a data layer. In someembodiments, individual application server modules 214 are used toimplement the functionality associated with various applications 120and/or services provided by the social networking service.

As shown in FIG. 2, the data layer may include several databases, suchas a profile database 218 for storing profile data, including bothmember profile data and profile data for various organizations (e.g.,companies, schools, etc.). Consistent with some embodiments, when aperson initially registers to become a member of the social networkingservice, the person will be prompted to provide some personalinformation, such as his or her name, age (e.g., birthdate), gender,interests, contact information, home town, address, spouse's and/orfamily members' names, educational background (e.g., schools, majors,matriculation and/or graduation dates, etc.), employment history,skills, professional organizations, and so on. This information isstored, for example, in the profile database 218. Once registered, amember may invite other members, or be invited by other members, toconnect via the social networking service. A “connection” may constitutea bilateral agreement by the members, such that both members acknowledgethe establishment of the connection. Similarly, in some embodiments, amember may elect to “follow” another member. In contrast to establishinga connection, the concept of “following” another member typically is aunilateral operation and, at least in some embodiments, does not requireacknowledgement or approval by the member that is being followed. Whenone member follows another, the member who is following may receivestatus updates (e.g., in an activity or content stream) or othermessages published by the member being followed, or relating to variousactivities undertaken by the member being followed. Similarly, when amember follows an organization, the member becomes eligible to receivemessages or status updates published on behalf of the organization. Forinstance, messages or status updates published on behalf of anorganization that a member is following will appear in the member'spersonalized data feed, commonly referred to as an activity stream orcontent stream. In any case, the various associations and relationshipsthat the members establish with other members, or with other entitiesand objects, are stored and maintained within a social graph in a socialgraph database 220.

As members interact with the various applications 120, services, andcontent made available via the social networking service, the members'interactions and behavior (e.g., content viewed, links or buttonsselected, messages responded to, etc.) may be tracked, and informationconcerning the members' activities and behavior may be logged or stored,for example, as indicated in FIG. 2, by the member activity and behaviordatabase 222. This logged activity information may then be used by thesearch engine 216 to determine search results for a search query.

In some embodiments, the databases 218, 220, and 222 may be incorporatedinto the database(s) 126 in FIG. 1. However, other configurations arealso within the scope of the present disclosure.

Although not shown, in some embodiments, the social networking servicesystem 210 provides an API module via which applications 120 andservices can access various data and services provided or maintained bythe social networking service. For example, using an API, an applicationmay be able to request and/or receive one or more navigationrecommendations. Such applications 120 may be browser-based applications120, or may be operating system-specific. In particular, someapplications 120 may reside and execute (at least partially) on one ormore mobile devices (e.g., phone or tablet computing devices) with amobile operating system. Furthermore, while in many cases theapplications 120 or services that leverage the API may be applications120 and services that are developed and maintained by the entityoperating the social networking service, nothing other than data privacyconcerns prevents the API from being provided to the public or tocertain third parties under special arrangements, thereby making thenavigation recommendations available to third party applications 128 andservices.

Although the search engine 216 is referred to herein as being used inthe context of a social networking service, it is contemplated that itmay also be employed in the context of any website or online services.Additionally, although features of the present disclosure are referredto herein as being used or presented in the context of a web page, it iscontemplated that any user interface view (e.g., a user interface on amobile device or on desktop software) is within the scope of the presentdisclosure.

In an example embodiment, when member profiles are indexed, forwardsearch indexes are created and stored. The search engine 216 facilitatesthe indexing and searching for content within the social networkingservice, such as the indexing and searching for data or informationcontained in the data layer, such as profile data (stored, e.g., in theprofile database 218), social graph data (stored, e.g., in the socialgraph database 220), and member activity and behavior data (stored,e.g., in the member activity and behavior database 222). The searchengine 216 may collect, parse, and/or store data in an index or othersimilar structure to facilitate the identification and retrieval ofinformation in response to received queries for information. This mayinclude, but is not limited to, forward search indexes, invertedindexes, N-gram indexes, and so on.

FIG. 3 is a block diagram illustrating an application server module 214of FIG. 2 in more detail. While in many embodiments the applicationserver module 214 will contain many subcomponents used to performvarious different actions within the social networking system, in FIG. 3only those components that are relevant to the present disclosure aredepicted. Here, an ingestion platform 300 obtains information from theprofile database 218, the social graph database 220, and the memberactivity and behavior database 222 relevant to a member and/or searcheridentified by a user interface server component 302. The user interfaceserver component 302 communicates with a user interface client component304 located on a client device 306 to obtain this identificationinformation. The details of the user interface client component 304 willbe described in more detail below, but generally a user, known hereafteras a searcher, of the user interface client component 304 may perform asearch or otherwise generate a search that provides search results ofmembers of the social networking service with whom the searcher may wishto communicate. Information about each of these members identified inthe search results may then be communicated via the user interfaceserver component 302 to the ingestion platform 300, which can use theidentifications to retrieve the appropriate information corresponding tothose members from the profile database 218, the social graph database220, and the member activity and behavior database 222. As will bediscussed in more detail below, in some example embodiments, informationabout the searcher, such as a recruiter, may also be relevant to acommunication reply score calculation also described later. As such, anidentification of the searcher may also be communicated via the userinterface server component 302 to the ingestion platform 300, which canuse the identifications to retrieve the appropriate informationcorresponding to the searcher from the profile database 218, the socialgraph database 220, and the member activity and behavior database 222.

The ingestion platform 300 may then provide the relevant informationfrom the profile database 218, the social graph database 220, and themember activity and behavior database 222 to a communication reply scoregenerator 308, which acts to calculate a communication reply score foreach identified member. In embodiments where the identification of thesearcher is relevant to this score generation, the communication replyscore may be thought of as being for each identified member/identifiedsearcher pair, and thus a different communication reply score may beassigned to the same member if a different searcher is involved.

The calculated communication reply score may then be passed from thecommunication reply score generator 308 to the user interface servercomponent 302, which acts to cause the user interface client component304 to display an indication to the user/searcher of the score for eachrelevant member with which the user/search the user/searcher ispresented. The form that this indication will take will be described inmore detail below.

FIG. 4 is a block diagram illustrating the communication reply scoregenerator 308 of FIG. 3 in more detail, in accordance with an exampleembodiment. In a training component 400, sample member profiles 402 andsample member activity and behavior information 404 are fed to a featureextractor 406, which acts to extract curated features 408 from thesample member profiles 402 and sample member activity and behaviorinformation 404. Different features may be extracted depending uponwhether the member profile is assumed to be that of a prospective searchresult or that of a prospective searcher.

In an example embodiment, the curated features 408 are then used totrain a supervised machine learning algorithm 410 to calculate aconfidence score that indicates the confidence that the targeted memberreplied to the searcher. This training may include providing samplemember labels 412 to the machine learning algorithm 410. Each label is abinary variable which indicates, in a case where a searcher sent anemail to a candidate, whether the candidate replied or not.

In a communication reply score calculation engine 414, candidate records416 are fed to a feature extractor 418, which acts to extract curatedfeatures 420 from the candidate records 416. The candidate records 416include member profile information and member activity and behaviorinformation extracted by the ingestion platform 300, which can use theidentifications from the user interface server component 302 to retrievethe appropriate information corresponding to those members from theprofile database 218, the social graph database 220, and the memberactivity and behavior database 222. The curated features 420 are thenused as input to a communication reply score model 422, which acts toprovide communication reply scores for member identified by the userinterface server component 302.

It should be noted that while the feature extractor 408 and the featureextractor 418 are depicted as separate components, in some exampleembodiments they may be the same component. Additionally, a large numberof different types of features could be extracted using the featureextractors 408 and 418.

In an example embodiment, features extracted by the feature extractor406 and/or feature extractor 418 include, but are not limited to:

1) Total page views by the member during sessions on the social network2) Inbox page views by the member during sessions on the social network3) News feed impressions by the member during sessions on the socialnetwork4) Number of address book uploads by the member during sessions on thesocial network5) Ad impressionsThe various metrics may also be gathered and weighted based on howrecent the data is. For example, total page views by a member may notinclude all member page views for all time, but may be limited to onlymember page views within the last month (indicating recent activity bythe member), and may be weighted such that member page views within thelast week have the most impact on the score (indicating even more recentactivity by the member).

The communication reply score model 422 may be trained specifically forthe type of communication reply desired. For example, as describedabove, one example use case involves recruiters wanting to know thelikelihood that a prospective job candidate will respond to an emailcommunication sent from the recruiter via the social networking service.Thus, in this case, the communication reply score model 422 may betrained based on features relevant to whether a member will be likely torespond to a job solicitation communication. On the other hand, if thecommunication reply score model 422 is used in a different use case,such as where a salesperson wants to know the likelihood that aprospective sales lead will respond to an email communication sent bythe salesperson via the social networking system, the communicationreply score model 422 may be trained using different features relevantto whether a member will be likely to respond to a sales solicitationcommunication.

As described briefly above, in an example embodiment, a user interfaceis provided that aids the searcher in efficiently discovering whether ornot members are likely to respond to a communication. The user interfacemay include a user interface server component 302 and a user interfaceclient component 304. Distribution of functions between the userinterface server component 302 and the user interface client component304 can vary based on the implementation of the user interface. In anexample embodiment, the user interface includes a service running in aweb browser, and thus the user interface server component 302 may bethought of as a web server while the user interface client component 304may comprise a web page (or a series of web pages) distributed by theweb server. The web page may include elements that, when selected, causeinformation to be passed to the user interface server component 302,which may then generate an updated web page to be displayed by the webbrowser. In this way, for example, searches may be performed by asearcher selecting one or more elements of the web page (or providingother input in the web page), which is then passed to the user interfaceserver component 302 which, after obtaining results responsive to thesearch, adds an indication of the communication reply score for eachmember in the results and generates a web page with the results andscore indications for display in the user interface client component304. In another example embodiment, the user interface client component304 may include a plug-in to a web browser.

In an example embodiment, results responsive to a search by a searcherare presented in the same way they ordinarily would be; namely, they maybe presented based on an algorithm that ranks the search results basedon various factors. Rather than reorder the results based on, forexample, communication reply score, in an example embodiment, theexisting ranking of the search results is maintained while an indicationis provided next to each search result providing the searcher withinformation as to the communication reply score for the correspondingmember.

FIG. 5 is a screen capture illustrating an example user interface 500,in accordance with an example embodiment. Here, the user interfacepresents search results in window 502. It should be noted that whilethese results are called search results, in some cases the results arenot produced in response to a specific search provided by a searcher butare produced in response to “browsing” by the searcher—namely where thesearcher navigates groupings of member profiles by category or even ispresented with random member results. In FIG. 5, no specific searchquery is provided by the searcher, and yet a series of member searchresults 504A-504E are presented as results. As discussed above, the userinterface client component 304 does not reorder these member searchresults 504A-504E based on communication reply score, but ratherprovides indicators 506A-506E of the general range in which thecommunication reply score lies.

Here, the general ranges are divided into three groups ‘highlikelihood,” “moderate likelihood,” and “low likelihood” of respondingto an electronic communication from the searcher. These groupings aredefined based on a comparison to historical communication reply scorescalculated for members. Specifically, an average communication replyscore across many members may be calculated. Scores within a particularrange of this average communication reply score may be placed in the“moderate likelihood” group, with scores above this range being placedin the “high likelihood” group and scores below this range placed in the“low likelihood group.” In an example embodiment, the range for the“moderate likelihood group” includes only those scores at or within aparticular range above the average communication reply score.

Thus, the grouping of a particular score is based on the relativerelationship between the score and the average score, as opposed to, forexample, based on an absolute range (e.g., scores above a certain presetlevel corresponding to a particular group).

In an alternative embodiment, the score groups are defined based on thehistorical communication response label data and their correspondingcommunication reply scores. For example, it may be noted that inhistorical data, when the communication reply score is greater than 0.3,the response rate is twice as high than the average response rate, andthus may be defined as a “high likelihood” group, while for acommunication reply score less than 0.2, the response rate is lower thanthe average response rate, and thus may be defined as a “low likelihood”group. The group for scores between 0.2 and 0.3 may then be defined as a“moderate likelihood” group. Note that the score range can varies bydifferent application, and also may change over time.

The indicators 506A-506E include two parts, a color and a bar chartelement. The color is a color chosen to represent the groupingcorresponding to the particular score. For example, red may representthe “low likelihood group,” yellow the “moderate likelihood group,” andgreen the “high likelihood” group. Likewise, the bar chart alsorepresents the grouping corresponding to the particular score. Forexample, “low likelihood” may be represented by a bar chart where thebar is only ¼ of the way filled in, “moderate likelihood” may berepresented by a bar chart where the bar is slightly over ½ of the wayfilled in, and “high likelihood” may be represented by a bar chart wherethe bar is entirely filled in. This is in contrast, for example, tohaving the bar represent the absolute communications reply score. Thereason for this is that it is not uncommon for an absolutecommunications reply score to be assigned that is less than what asearcher might incorrectly perceive as a “low” score, and yet that scoremay still be relatively high. For example, if the score represents theprobability (between 0 and 1) that the member will respond to anelectronic communication, then the searcher may perceive that somethingabove 0.8 may be necessary in order for that score to be deemed high,yet in reality given the low percentage of electronic solicitations ofany type that receive responses, a probability of 0.5 might actually bea fantastic score. If the bar chart were to represent the 0.5probability in absolute terms, the bar would only be half filled in,giving an incorrect impression to the searcher.

The indications may continue to appear as the searcher drills down onsearch results. FIG. 6 is a screen capture illustrating a user interface600, in accordance with another example embodiment. Here, the searcherhas selected member search result 504A of FIG. 5, resulting in thatmember's profile 602 being displayed. Notably, the member's indicator506A is also displayed in this profile 602.

FIG. 7 is a screen capture illustrating a user interface 700, inaccordance with another example embodiment. Here, the searcher hasperformed a specific search query for members matching certain criteria(specified by filters 702 and by a search query 704). The member searchresults 706A-706C therefore reflect members matching the filters 702 andsearch query 704, and are presented in an order that is defined by asearch algorithm, and not based on the communication reply score for themembers corresponding to the member search results 706A-706C. Thus, forexample, member search result 706B is presented above member searchresult 706C, despite the fact that indication 708B and indication 708Cindicate that the member corresponding to member search result 706B hasa lower communication reply score than the member corresponding tomember search result 706C.

In another example embodiment, the application server module 214 mayautomatically periodically refresh the communication reply scores. Forexample, a searcher may be able to “favorite” particular members who fita particular criteria but for one reason or another have lowercommunication reply scores. The system may then periodically refreshthose members' communication reply scores and notify the searcher if amember's communication reply score significantly changes. Thus, in thecase where a particular member had no interest in receiving jobsolicitations but something changes and that member suddenly hasinterest in receiving job solicitations (e.g., being turned down for apromotion), the updated communication reply score may be used to alertthe searcher that the member may now be a good candidate to send acommunication to, even though they were not before.

As described above, the instant disclosure may be expanded beyond theuse case involving recruiters looking for job applicants to other usercases where a searcher wants to know the probability that acommunication will garner a response from one of the memberscorresponding to a search result. One additional such use case is in therealm of dating web sites. In such web sites, the social networkingservice described above may be thought of as the dating service. Thecommunication reply score model 402 may be trained based on featuresthat are relevant to whether a potential dating match would be likely torespond to an electronic communication from the searcher. This may beuseful for searchers concerned with saving time or improving efficiencyby not needing to create communications to low-probability responders,but also can be useful for those members of the dating pool who are shy,have social anxiety issues, or otherwise may be very reluctant to sendcommunications for fear of“rejection” (even though the rejection may bepassive, in that the recipient may simply not respond).

FIG. 8 is a flow diagram illustrating a method 800, in accordance withan example embodiment, for providing an indication of a probability thata member of a social networking service will respond to an electroniccommunication sent via the social networking service. At operation 802,a plurality of sample member profiles and response labels of members ofthe social networking service, and activity and usage informationpertaining to actions taken by those members on the social networkingservice, are retrieved. Then a loop is begun for each sample memberprofile. At operation 804, the sample member profile is parsed toextract a first set of one or more features from the sample memberprofile. At operation 806, the activity and usage information pertainingto actions taken by the member on the social networking service areparsed to extract a second set of one or more features. At operation808, the extracted first set of features and second set of features arefed into a supervised machine learning algorithm to train acommunication reply score model based on the response labels, theextracted first set of features and the second set of features. Atoperation 810, it is determined if there are any more sample memberprofiles. If so, then the process loops back to operation 804.

If not, then at operation 812, a plurality of member search resultsproduced by actions performed in a user interface is obtained, with eachmember search result identifying a member of the social networkingservice. In some example embodiments these results include an orderingbased on a ranking of each member search result based on a searchengine. Then a loop is begun for the member identified in each of theplurality of member search results. At operation 814, a member profilecorresponding to the member is parsed to extract a third set of one ormore features from the member profile. At operation 816, activity andusage information pertaining to actions taken by the members on thesocial networking service is parsed to extract a fourth set of one ormore features. In some example embodiments, the first set of features isidentical to the third set of features and the second set of features isidentical to the fourth set of features. At operation 818, the extractedthird set of features and fourth set of features are inputted into thecommunication reply score model to generate a communication reply scorereflecting a probability that the member will respond to an emailcommunication from the searcher. At operation 820, it is determined ifthis is the last member search result. If not, then the process loopsback to operation 814.

If so, then at operation 822, the member search results are presentedvisually in the user interface, with each member search result beingpresented with a visual indication of the corresponding members'communication reply score. In some example embodiments, the orderingobtained with the search results is maintained in the presentation inoperation 822, regardless of the communication reply scores of thecorresponding members.

At operation 824, a selection of one or more members is received fromthe user interface as favorites. At operation 826, operations 814-818are periodically repeated for the corresponding member. At operation828, the searcher is notified if a communication reply score for thecorresponding member changes significantly. In an example embodiment,the communication reply score is deemed to have changed significantly ifthe change causes the score to fall within a different grouping. Inanother example embodiment, the communication reply score is deemed tohave changed significantly if the change is greater than a preset scoredifferential. In another example embodiment, the communication replyscore is deemed to have changed significantly if the change is greaterthan a preset percentage.

It should be noted that in an alternative example embodiment, ratherthan perform the scoring just on the members that are included as searchor browse results of a searcher, scoring is performed on all members ona periodic basis (e.g., weekly). This allows for faster processing ofsearches as scores will not need to be computed on-the-fly when a searchis performed.

In embodiments where the supervised machine learning algorithm takesinto account information about the searcher in its score calculation,there may be additional steps involved in retrieving a plurality ofsample searcher member profiles of members of the social networkingservice, and activity and usage information pertaining to actions takenby those searchers on the social networking service, for each samplesearcher member profile: parsing the sample searcher member profile toextract a fifth set of one or more features from the sample searchermember profile and parsing the activity and usage information pertainingto actions taken by those searchers on the social networking service toextract a sixth set of one or more features, and feeding the extractedfifth set of features and sixth set of features into a supervisedmachine learning algorithm to train a communication reply score modelbased on the extracted fifth set of features and the sixth set offeatures. Furthermore, once the communication reply score model istrained, there may be additional steps involved in obtaining anidentification of the searcher from the user interface, parsing a memberprofile corresponding to the searcher to extract a seventh set of one ormore features from the member profile and parsing activity and usageinformation pertaining to actions taken by the searcher on the socialnetworking service to extract an eighth set of one or more features; andinputting the extracted seventh set of features and eight set offeatures into the communication reply score model to generate thecommunication reply score reflecting a probability that the member willrespond to an email communication from the searcher.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Machine and Software Architecture

The modules, methods, applications, and so forth described inconjunction with FIGS. 1-8 are implemented in some embodiments in thecontext of a machine and an associated software architecture. Thesections below describe representative software architecture(s) andmachine (e.g., hardware) architecture(s) that are suitable for use withthe disclosed embodiments.

Software architectures are used in conjunction with hardwarearchitectures to create devices and machines tailored to particularpurposes. For example, a particular hardware architecture coupled with aparticular software architecture will create a mobile device, such as amobile phone, tablet device, or so forth. A slightly different hardwareand software architecture may yield a smart device for use in the“internet of things,” while yet another combination produces a servercomputer for use within a cloud computing architecture. Not allcombinations of such software and hardware architectures are presentedhere, as those of skill in the art can readily understand how toimplement the inventive subject matter in different contexts from thedisclosure contained herein.

Software Architecture

FIG. 9 is a block diagram 900 illustrating a representative softwarearchitecture 902, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 9 is merely a non-limiting exampleof a software architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 902 may be executing onhardware such as a machine 1000 of FIG. 10 that includes, among otherthings, processors 1010, memory/storage 1030, and I/O components 1050. Arepresentative hardware layer 904 is illustrated and can represent, forexample, the machine 1000 of FIG. 10. The representative hardware layer904 comprises one or more processing units 906 having associatedexecutable instructions 908. The executable instructions 908 representthe executable instructions of the software architecture 902, includingimplementation of the methods, modules, and so forth of FIGS. 1-8. Thehardware layer 904 also includes memory and/or storage modules 910,which also have the executable instructions 908. The hardware layer 904may also comprise other hardware 912, which represents any otherhardware of the hardware layer 904, such as the other hardwareillustrated as part of the machine 1000.

In the example architecture of FIG. 9, the software architecture 902 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 902 mayinclude layers such as an operating system 914, libraries 916,frameworks/middleware 918, applications 920, and a presentation layer944. Operationally, the applications 920 and/or other components withinthe layers may invoke API calls 924 through the software stack andreceive responses, returned values, and so forth, illustrated asmessages 926, in response to the API calls 924. The layers illustratedare representative in nature and not all software architectures have alllayers. For example, some mobile or special purpose operating systemsmay not provide a layer of frameworks/middleware 918, while others mayprovide such a layer. Other software architectures may includeadditional or different layers.

The operating system 914 may manage hardware resources and providecommon services. The operating system 914 may include, for example, akernel 928, services 930, and drivers 932. The kernel 928 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 928 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 930 may provideother common services for the other software layers. The drivers 932 maybe responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 932 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 916 may provide a common infrastructure that may beutilized by the applications 920 and/or other components and/or layers.The libraries 916 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than byinterfacing directly with the underlying operating system 914functionality (e.g., kernel 928, services 930, and/or drivers 932). Thelibraries 916 may include system 934 libraries (e.g., C standardlibrary) that may provide functions such as memory allocation functions,string manipulation functions, mathematic functions, and the like. Inaddition, the libraries 916 may include API 936 libraries such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 916 may also include a wide variety of otherlibraries 938 to provide many other APIs to the applications 920 andother software components/modules.

The frameworks 918 (also sometimes referred to as middleware) mayprovide a higher-level common infrastructure that may be utilized by theapplications 920 and/or other software components/modules. For example,the frameworks 918 may provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 918 may provide a broad spectrum of otherAPIs that may be utilized by the applications 920 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 920 include built-in applications 940 and/or thirdparty applications 942. Examples of representative built-in applications940 may include, but are not limited to, a contacts application, abrowser application, a book reader application, a location application,a media application, a messaging application, and/or a game application.The third party applications 942 may include any of the built-inapplications as well as a broad assortment of other applications. In aspecific example, the third party application 942 (e.g., an applicationdeveloped using the Android™ or iOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as iOS™,Android™, Windows® Phone, or other mobile operating systems. In thisexample, the third party application 942 may invoke the API calls 924provided by the mobile operating system such as the operating system 914to facilitate functionality described herein.

The applications 920 may utilize built-in operating system 914 functions(e.g., kernel 928, services 930, and/or drivers 932), libraries 916(e.g., system 934, APIs 936, and other libraries 938), andframeworks/middleware 918 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 944. In these systems, the application/module“logic” can be separated from the aspects of the application/module thatinteract with a user.

Some software architectures utilize virtual machines. In the example ofFIG. 9, this is illustrated by a virtual machine 948. A virtual machinecreates a software environment where applications/modules can execute asif they were executing on a hardware machine (such as the machine 1000of FIG. 10, for example). A virtual machine is hosted by a hostoperating system (e.g., operating system 914 in FIG. 9) and typically,although not always, has a virtual machine monitor 946, which managesthe operation of the virtual machine as well as the interface with thehost operating system (e.g., operating system 914). A softwarearchitecture executes within the virtual machine 948, such as anoperating system 950, libraries 952, frameworks/middleware 954,applications 956, and/or a presentation layer 958. These layers ofsoftware architecture executing within the virtual machine 948 can bethe same as corresponding layers previously described or may bedifferent.

Example Machine Architecture and Machine-Readable Medium

FIG. 10 is a block diagram illustrating components of a machine 1000,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1016 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. Theinstructions transform the general, non-programmed machine into aparticular machine programmed to carry out the described and illustratedfunctions in the manner described. In alternative embodiments, themachine 1000 operates as a standalone device or may be coupled (e.g.,networked) to other machines. In a networked deployment, the machine1000 may operate in the capacity of a server machine or a client machinein a server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1000 maycomprise, but not be limited to, a server computer, a client computer, aPC, a tablet computer, a laptop computer, a netbook, a set-top box(STB), a personal digital assistant (PDA), an entertainment mediasystem, a cellular telephone, a smart phone, a mobile device, a wearabledevice (e.g., a smart watch), a smart home device (e.g., a smartappliance), other smart devices, a web appliance, a network router, anetwork switch, a network bridge, or any machine capable of executingthe instructions 1016, sequentially or otherwise, that specify actionsto be taken by the machine 1000. Further, while only a single machine1000 is illustrated, the term “machine” shall also be taken to include acollection of machines 1000 that individually or jointly execute theinstructions 1016 to perform any one or more of the methodologiesdiscussed herein.

The machine 1000 may include processors 1010, memory/storage 1030, andI/O components 1050, which may be configured to communicate with eachother such as via a bus 1002. In an example embodiment, the processors1010 (e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) processor, a Complex Instruction Set Computing (CISC)processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), anotherprocessor, or any suitable combination thereof) may include, forexample, a processor 1012 and a processor 1014 that may execute theinstructions 1016. The term “processor” is intended to includemulti-core processors that may comprise two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously. Although FIG. 10 shows multipleprocessors 1010, the machine 1000 may include a single processor with asingle core, a single processor with multiple cores (e.g., a multi-coreprocessor), multiple processors with a single core, multiple processorswith multiples cores, or any combination thereof.

The memory/storage 1030 may include a memory 1032, such as a mainmemory, or other memory storage, and a storage unit 1036, bothaccessible to the processors 1010 such as via the bus 1002. The storageunit 1036 and memory 1032 store the instructions 1016 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1016 may also reside, completely or partially, within thememory 1032, within the storage unit 1036, within at least one of theprocessors 1010 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1000. Accordingly, the memory 1032, the storage unit 1036, and thememory of the processors 1010 are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)), and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store the instructions 1016. Theterm “machine-readable medium” shall also be taken to include anymedium, or combination of multiple media, that is capable of storinginstructions (e.g., instructions 1016) for execution by a machine (e.g.,machine 1000), such that the instructions, when executed by one or moreprocessors of the machine (e.g., processors 1010), cause the machine toperform any one or more of the methodologies described herein.Accordingly, a “machine-readable medium” refers to a single storageapparatus or device, as well as “cloud-based” storage systems or storagenetworks that include multiple storage apparatus or devices. The term“machine-readable medium” excludes signals per se.

The I/O components 1050 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1050 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1050 may include many other components that are not shown in FIG. 10.The I/O components 1050 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1050 mayinclude output components 1052 and input components 1054. The outputcomponents 1052 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1054 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1050 may includebiometric components 1056, motion components 1058, environmentalcomponents 1060, or position components 1062, among a wide array ofother components. For example, the biometric components 1056 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1058 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1060 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detect concentrations of hazardous gases for safetyor to measure pollutants in the atmosphere), or other components thatmay provide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1062 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1050 may include communication components 1064operable to couple the machine 1000 to a network 1080 or devices 1070via a coupling 1082 and a coupling 1072, respectively. For example, thecommunication components 1064 may include a network interface componentor other suitable device to interface with the network 1080. In furtherexamples, the communication components 1064 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1070 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1064 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1064 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1064, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 1080may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a WAN,a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet,a portion of the Internet, a portion of the Public Switched TelephoneNetwork (PSTN), a plain old telephone service (POTS) network, a cellulartelephone network, a wireless network, a Wi-Fi® network, another type ofnetwork, or a combination of two or more such networks. For example, thenetwork 1080 or a portion of the network 1080 may include a wireless orcellular network and the coupling 1082 may be a Code Division MultipleAccess (CDMA) connection, a Global System for Mobile communications(GSM) connection, or another type of cellular or wireless coupling. Inthis example, the coupling 1082 may implement any of a variety of typesof data transfer technology, such as Single Carrier Radio TransmissionTechnology (1×RTT), Evolution-Data Optimized (EVDO) technology, GeneralPacket Radio Service (GPRS) technology, Enhanced Data rates for GSMEvolution (EDGE) technology, third Generation Partnership Project (3GPP)including 3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long range protocols, or other data transfertechnology.

The instructions 1016 may be transmitted or received over the network1080 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1064) and utilizing any one of a number of well-known transfer protocols(e.g., HTTP). Similarly, the instructions 1016 may be transmitted orreceived using a transmission medium via the coupling 1072 (e.g., apeer-to-peer coupling) to the devices 1070. The term “transmissionmedium” shall be taken to include any intangible medium that is capableof storing, encoding, or carrying the instructions 1016 for execution bythe machine 1000, and includes digital or analog communications signalsor other intangible media to facilitate communication of such software.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A system comprising: a non-transitory computerreadable medium having instructions stored there on, which, whenexecuted by a processor, cause the system to: retrieve a plurality ofsample member profiles of members of the social networking service and aplurality of sample member labels; for each sample member profile: parsethe sample member profile to extract a first set of one or more featuresfrom the sample member; feed the sample member labels and extractedfirst set of features into a supervised machine learning algorithm totrain a communication reply score model based on the extracted first setof features; obtain a plurality of member search results, each membersearch result identifying a member of the social networking service; forthe member identified in each of the plurality of member search results:parse a member profile corresponding to the member to extract a secondset of one or more features from the member profile; input the extractedsecond set of features into the communication reply score model togenerate a communication reply score reflecting a probability that themember will respond to an email communication from a searcher.
 2. Thesystem of claim 1, wherein the second set of features is identical tothe first set of features.
 3. The system of claim 1, wherein theobtaining the plurality of member search results includes obtaining anordering of the plurality of member search results, the ordering basedon a ranking of each member search result based on a search algorithm;and the instructions further cause the system to cause the member searchresults to be displayed visually in an order reflecting the ordering,regardless of the communication reply scores of the correspondingmembers.
 4. The system of claim 1, wherein the instructions furthercause the system to: receive a selection of one or more members from theuser interface as favorites; and for each of the one or more membersselected as favorites, periodically repeat the parsing and inputting forthe corresponding member and notifying the searcher if a communicationreply score for the corresponding member changes significantly.
 5. Thesystem of claim 1, wherein the instructions further cause the system to:group each member communication reply score into a category based on itsrelationship to an average communication reply score among a pluralityof members; and display a visual indication of the correspondinggrouping for the member communication reply score for the correspondingmember.
 6. The method of claim 1, wherein the instructions further causethe system to: retrieve a plurality of sample searcher member profilesof members of the social networking service, and activity and usageinformation pertaining to actions taken by those searchers on the socialnetworking service; for each sample searcher member profile: parse thesample searcher member profile to extract a third set of one or morefeatures from the sample searcher member profile; and feed the extractedthird set of features into the supervised machine learning algorithm totrain the communication reply score model based on the extracted thirdset of features.
 7. The method of claim 6, wherein the instructionsfurther cause the system to: obtain an identification of the searcherfrom the user interface; parse a member profile corresponding to thesearcher to extract a fourth set of one or more features from the memberprofile; and input the extracted fourth set of features into thecommunication reply score model to generate the communication replyscore reflecting a probability that the member will respond to an emailcommunication from the searcher.
 8. A computer-implemented method forproviding an indication of a probability that a member of an onlineservice will respond to an electronic communication, the methodcomprising: retrieving a plurality of sample member profiles of membersof the social networking service and a plurality of sample memberlabels; for each sample member profile: parsing the sample memberprofile to extract a first set of one or more features from the samplemember; feeding the sample member labels and extracted first set offeatures into a supervised machine learning algorithm to train acommunication reply score model based on the extracted first set offeatures; obtaining a plurality of member search results, each membersearch result identifying a member of the social networking service; forthe member identified in each of the plurality of member search results:parsing a member profile corresponding to the member to extract a secondset of one or more features from the member profile; inputting theextracted second set of features into the communication reply scoremodel to generate a communication reply score reflecting a probabilitythat the member will respond to an email communication from a searcher.9. The method of claim 8, wherein the second set of features isidentical to the first set of features.
 10. The method of claim 8,wherein the obtaining the plurality of member search results includesobtaining an ordering of the plurality of member search results, theordering based on a ranking of each member search result based on asearch algorithm; and the method further includes causing the membersearch results to be displayed visually in an order reflecting theordering, regardless of the communication reply scores of thecorresponding members.
 11. The method of claim 8, further comprising:receiving a selection of one or more members from the user interface asfavorites; and for each of the one or more members selected asfavorites, periodically repeating the parsing and inputting for thecorresponding member and notifying the searcher if a communication replyscore for the corresponding member changes significantly.
 12. The methodof claim 8, further comprising: grouping each member communication replyscore into a category based on its relationship to an averagecommunication reply score among a plurality of members; and displaying avisual indication of the corresponding grouping for the membercommunication reply score for the corresponding member.
 13. The methodof claim 8, further comprising: retrieving a plurality of samplesearcher member profiles of members of the social networking service,and activity and usage information pertaining to actions taken by thosesearchers on the social networking service; for each sample searchermember profile: parsing the sample searcher member profile to extract athird set of one or more features from the sample searcher memberprofile; and feeding the extracted third set of features into thesupervised machine learning algorithm to train the communication replyscore model based on the extracted third set of features.
 14. The methodof claim 13, further comprising: obtaining an identification of thesearcher from the user interface; parsing a member profile correspondingto the searcher to extract a fourth set of one or more features from themember profile; and inputting the extracted fourth set of features intothe communication reply score model to generate the communication replyscore reflecting a probability that the member will respond to an emailcommunication from the searcher.
 15. A non-transitory machine-readablestorage medium comprising instructions, which when implemented by one ormore machines, cause the one or more machines to perform operationscomprising: retrieving a plurality of sample member profiles of membersof the social networking service and a plurality of sample memberlabels; for each sample member profile: parsing the sample memberprofile to extract a first set of one or more features from the samplemember; feeding the sample member labels and extracted first set offeatures into a supervised machine learning algorithm to train acommunication reply score model based on the extracted first set offeatures; obtaining a plurality of member search results, each membersearch result identifying a member of the social networking service; forthe member identified in each of the plurality of member search results:parsing a member profile corresponding to the member to extract a secondset of one or more features from the member profile; inputting theextracted second set of features into the communication reply scoremodel to generate a communication reply score reflecting a probabilitythat the member will respond to an email communication from a searcher.16. The non-transitory machine-readable storage medium of claim 15,wherein the second set of features is identical to the first set offeatures.
 17. The non-transitory machine-readable storage medium ofclaim 15, wherein the obtaining the plurality of member search resultsincludes obtaining an ordering of the plurality of member searchresults, the ordering based on a ranking of each member search resultbased on a search algorithm; and wherein the instructions furthercomprise: causing the member search results to be displayed visually inan order reflecting the ordering, regardless of the communication replyscores of the corresponding members.
 18. The non-transitorymachine-readable storage medium of claim 15, wherein the instructionsfurther comprise: receiving a selection of one or more members from theuser interface as favorites; and for each of the one or more membersselected as favorites, periodically repeating the parsing and inputtingfor the corresponding member and notifying the searcher if acommunication reply score for the corresponding member changessignificantly.
 19. The non-transitory machine-readable storage medium ofclaim 15, wherein the instructions further comprise: grouping eachmember communication reply score into a category based on itsrelationship to an average communication reply score among a pluralityof members; and displaying a visual indication of the correspondinggrouping for the member communication reply score for the correspondingmember.
 20. The non-transitory machine-readable storage medium of claim15, wherein the instructions further comprise: retrieving a plurality ofsample searcher member profiles of members of the social networkingservice, and activity and usage information pertaining to actions takenby those searchers on the social networking service; for each samplesearcher member profile: parsing the sample searcher member profile toextract a third set of one or more features from the sample searchermember profile; and feeding the extracted third set of features into thesupervised machine learning algorithm to train the communication replyscore model based on the extracted third set of features.